Genomic and healthcare dynamics of nosocomial SARS-CoV-2 transmission
- University of Manchester, United Kingdom
- Manchester University NHS Foundation Trust, United Kingdom
- Manchester Centre for Genomic Medicine, United Kingdom
Abstract
Understanding the effectiveness of infection control methods in reducing and preventing SARS-CoV-2 transmission in healthcare settings is of high importance. We sequenced SARS-CoV-2 genomes for patients and healthcare workers (HCWs) across multiple geographically distinct UK hospitals, obtaining 173 high-quality SARS-CoV-2 genomes. We integrated patient movement and staff location data into the analysis of viral genome data to understand spatial and temporal dynamics of SARS-CoV-2 transmission. We identified eight patient contact clusters (PCC) with significantly increased similarity in genomic variants compared to non-clustered samples. Incorporation of HCW location further increased the number of individuals within PCCs and identified additional links in SARS-CoV-2 transmission pathways. Patients within PCCs carried viruses more genetically identical to HCWs in the same ward location. SARS-CoV-2 genome sequencing integrated with patient and HCW movement data increases identification of outbreak clusters. This dynamic approach can support infection control management strategies within the healthcare setting.
Data availability
All genome sequencing datasets have been shared with COG-UK.
Article and author information
Author details
Jamie M Ellingford
Funding
Health Education England
- Jamie M Ellingford
Manchester Biomedical Research Centre
- William G Newman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Niel Hens, Hasselt University & University of Antwerp, Belgium
Ethics
Human subjects: The study was conducted to investigate hospital outbreak investigation/surveillance; individual patient consent or ethical approvals were not required. The study protocol was approved by the Manchester Biomedical Research Centre COVID-19 rapid response group and the Manchester University NHS Foundation Trust Executive Committee. All samples and data collected were part of routine care or hospital operational policy. No patient-identifiable/individual identifiable data are presented.
Version history
- Received: December 4, 2020
- Accepted: March 16, 2021
- Accepted Manuscript published: March 17, 2021 (version 1)
- Version of Record published: March 29, 2021 (version 2)
Copyright
© 2021, Ellingford et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Genomic and healthcare dynamics of nosocomial SARS-CoV-2 transmission
eLife 10:e65453.
https://doi.org/10.7554/eLife.65453
Further reading
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- Medicine
- Microbiology and Infectious Disease
- Epidemiology and Global Health
- Immunology and Inflammation
eLife has published articles on a wide range of infectious diseases, including COVID-19, influenza, tuberculosis, HIV/AIDS, malaria and typhoid fever.
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- Evolutionary Biology
- Genetics and Genomics
A protein’s genetic architecture – the set of causal rules by which its sequence produces its functions – also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest – excluding the vast majority of possible genotypes and evolutionary trajectories – and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor’s specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor’s capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.
